code: def _bmm(cls, arg0, arg1):
      if not (isinstance(arg0, cls) and type(arg1) is torch.Tensor):
          return NotImplemented
  
      assert arg0.ndim == 3
      assert arg1.ndim == 3
  
      self = arg0
      b = arg1
  
      _, m, n = self.shape
      row_indices = self.__row_indices
      values = self.__values
      row_offsets = self.__row_offsets
      column_indices = self.__column_indices
  
      out = _csr_ops._spmm.apply(
          b,
          row_indices,
          values,
          row_offsets,
          column_indices,
          m,
          self.__transp_info,
      )
      return out
  
imports: import torch
  from xformers.ops import masked_matmul
  from xformers.sparse import _csr_ops
  from xformers.sparse.utils import _csr_to_coo, _dense3d_to_sparse, _diffsort, _get_transpose_info, _transpose_with_info
label_desc: Use image node_id 12 for calling the SparseCSRTensor obj's underlying member method code with example usage: obj._bmm(cls, arg0, arg1) and returns: out, NotImplemented